dashboard

Name: dashboard
Availability: InStock
Author: ReefBytes-Owner

Aggregate agent efficiency metrics from local logs into markdown summaries.

1|Updated Aug 16, 2025

Search Tags:#metrics #dashboard #consensus #data-visualization #log-analysis #model-usage #agent-logs

Ready to run

npx skills add https://github.com/ReefBytes-Owner/Manifest --skill dashboard-reefbytes-owner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dashboard
Source: https://github.com/ReefBytes-Owner/Manifest/tree/main/configs/claude/skills/dashboard
Command: npx skills add https://github.com/ReefBytes-Owner/Manifest --skill dashboard-reefbytes-owner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Visualize agent efficiency metrics to help teams quickly identify performance bottlenecks and error patterns.

Core Features & Use Cases

Aggregate task completion rates, consensus scores, and model usage to monitor agent health.
Generate markdown dashboards and summaries from local logs stored in .claude/.agent_outputs/.
Use cases include sprint reviews, incident postmortems, and capacity planning.

Quick Start

Run the dashboard skill to generate current metrics from the ~/.claude/.agent_outputs/ directory for the last 30 days.

Frequently Asked Questions about dashboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I visualize agent efficiency metrics from local logs?▼

To visualize agent efficiency metrics from local logs, you can parse the results_*.json and summary_*.md files stored in the .claude/.agent_outputs/ directory to generate markdown dashboards and summaries.

Can I generate a metrics dashboard for task completion rates and error patterns?▼

Yes, you can generate a metrics dashboard for task completion rates and error patterns by aggregating local agent logs to reveal performance trends over configurable time ranges.

How do I track consensus scores and model usage across multiple agents?▼

You can track consensus scores and model usage across multiple agents by running a skill that reads local agent output directories and aggregates the parsed data into markdown summaries.

What is the best way to prepare log data for a sprint review or capacity planning?▼

The best way to prepare log data for a sprint review or capacity planning is to aggregate task completion rates and model usage from local logs into a visual markdown dashboard, helping you quickly identify performance bottlenecks.

Does the dashboard skill require external dependencies to parse agent logs?▼

No, the dashboard skill requires no external dependencies to parse agent logs, as it reads local .claude/.agent_outputs/ data directly and applies safety checks during the parsing and aggregation process.